add Classification Report and confusion matrix

main.py

@@ -3,6 +3,7 @@
 from sklearn.naive_bayes import CategoricalNB
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score
+from utils import *
 
 K = 5
 random_seed = 0
@@ -32,6 +33,7 @@ def read_data():
 y_pred = clf.predict(X_test)
 acc = accuracy_score(y_test, y_pred)
 print('accuracy is: {}'.format(acc))
+save_cm_figs(y_test, y_pred, 'NB_ori')  # confusion matrix
 
 # X_2 = X[Y==2] #benign
 # X_4 = X[Y==4] #cancer

utils.py

@@ -0,0 +1,63 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.metrics import classification_report, confusion_matrix
+import itertools
+
+def plot_confusion_matrix(y_true, y_pred, classes = ['benign', 'cancer'],
+                          normalize=False,
+                          title='Confusion matrix',
+                          cmap=plt.cm.Blues):
+    """
+    This function prints and plots the confusion matrix.
+    Normalization can be applied by setting `normalize=True`.
+    """
+    print('Confusion Matrix')
+    cm = confusion_matrix(y_true, y_pred)
+    print('Classification Report')
+    target_names = ['benigh', 'cancer']
+    print(classification_report(y_true, y_pred, target_names=target_names))
+
+    if normalize:
+        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+        print("Normalized confusion matrix")
+    else:
+        print('Confusion matrix, without normalization')
+
+    print(cm)
+
+    plt.imshow(cm, interpolation='nearest', cmap=cmap)
+    plt.title(title)
+    plt.colorbar()
+    tick_marks = np.arange(len(classes))
+    plt.xticks(tick_marks, classes, rotation=45)
+    plt.yticks(tick_marks, classes)
+
+    fmt = '.2f' if normalize else 'd'
+    thresh = cm.max() / 2.
+    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
+        plt.text(j, i, format(cm[i, j], fmt),
+                 horizontalalignment="center",
+                 color="white" if cm[i, j] > thresh else "black")
+
+    plt.ylabel('True label')
+    plt.xlabel('Predicted label')
+    plt.tight_layout()
+
+
+def save_cm_figs(y_true, y_pred, arc, target_names = ['begign', 'cancer']):
+    # Compute confusion matrix
+
+    np.set_printoptions(precision=2)
+
+    # Plot non-normalized confusion matrix
+    plt.figure()
+    plot_confusion_matrix(y_true, y_pred, classes=target_names,
+                          title='Confusion matrix, without normalization')
+    plt.savefig('./result/conf_no_norm_{}.png'.format(arc), bbox_inches='tight')
+    # Plot normalized confusion matrix
+    plt.figure()
+    plot_confusion_matrix(y_true, y_pred, classes=target_names, normalize=True,
+                          title='Normalized confusion matrix')
+    plt.savefig('./result/conf_norm_{}.png'.format(arc), bbox_inches='tight')
+    plt.show()
+